ml-training-init-mcp

# ML Training Init MCP Server A Model Context Protocol (MCP) server that implements a sequential thinking pattern for ML training script generation with managed file constraints. ## Key Feature: Managed File System (1 Training + 2 Configs) **IMPORTANT**: This server enforces a managed file constraint system: - **1 Training Script**: The main ML training file that will be executed - **Up to 2 Config Files**: Optional configuration files (YAML, JSON, .env, etc.) - ALL operations work only with these managed files - Prevents agents from creating unnecessary files when stuck - Clearly identifies ML training workflows with [ML TRAINING WORKFLOW] tags ## Installation ```bash pip install -r requirements.txt ``` ## Running the Server ```bash python -m src.server ``` ## Usage with Claude Desktop Add to your Claude Desktop configuration: ```json { "mcpServers": { "ml-training-init": { "command": "python", "args": ["-m", "src.server"], "cwd": "/path/to/ml-training-init-mcp" } } } ``` ## Available Tools ### 1. `initialize_training_file` - Creates the main training script file - Takes: file_name, content, reference - Returns: file_path, file_type, managed_files status - AI agents use this for ML training workflows ### 2. `create_config_file` - Creates configuration files (max 2 allowed) - Supports: YAML, JSON, TOML, .env, .ini, etc. - Takes: file_name, content, config_type - Returns: file_path, file_type, managed_files status ### 3. `get_managed_files` - Lists all managed files (training + configs) - Shows file paths and names - Returns: training file info, config files list ### 4. `get_file_content` - Get content of a specific managed file - Takes: file_name - Returns: file_path, content, file_type ### 5. `get_current_file` - Quick access to training file content - Returns: file_path, content ### 6. `monitor_and_fix` - Fix errors in any managed file - Takes: error_trace, file_name (optional) - Auto-parses common Python/ML errors …